3.3.2 Distributed and Centralized Decision Fusion
Depending on the DSA design, local decision fusion can be communicated to distributed peer nodes or to a centralized arbitrator. Distributed and centralized techniques can estimate a more comprehensive spectrum map of the area of operation. Spectrum awareness can be local (node‐based), distributed (net‐based), or centralized (area‐of‐operation‐based).23 Spectrum awareness can be expressed as a spatial map showing areas of interference per each frequency band used in the area of operation or can be potentially used.
Let us consider the local interference estimation exemplified in Figure 3.7 for a local node. Let us also consider that this type of directional interference estimation is shared between peer nodes in a distributed cooperative MANET. Each node can fuse the directional spectrum interference estimation communicated from multiple peer nodes to produce a cooperative spectrum map of the area of interference as shown in Figure 3.10. Notice how the estimated interference area can be larger than the actual interference area as spectrum directional fusion may draw a circle around the estimated overlaid triangles.24 If this sensed frequency is the same frequency used by the MANET, cooperative decisions can be made to continue to use the frequency band or switch to a different frequency band based on the results of the decision fusion.
Figure 3.10 Cooperative distributed estimation of area of interference.
Figure 3.11 shows a case where a distributed cooperative MANET DSA technique estimated the interference area of the frequency band in use by the MANET, f1. The same DSA technique also estimated the (probed) frequency band f2, which is a potential frequency band to be used by the MANET. If the MANET trajectory25 is as shown in Figure 3.11, the distributed cooperative decision may continue to use f1 as the MANET will move away from the area where interference of f1 is detected and avoid using f2. This decision will occur because probing f2 as a potential frequency to use informed the DSA technique that the MANET will encounter interference if it is to switch to f2. The key point here is for the distributed cooperative MANET to overlay estimated interference areas of the frequency band in use with the estimated spatial areas of use of potential frequency bands and consider other factors such as trajectory to make the best spectrum use decision, which may include avoiding f2 and switching to a third frequency f3 or staying with f1. Recall that a good DSA design will attempt to avoid the rippling of DSA decisions.
Figure 3.11 Cooperative estimation of overlay spatial use of different frequency bands.
A centralized arbitrator can start by using a pool of frequency bands to assign to different heterogeneous networks in a spatially separated manner, as shown in the previous chapter in Figure 2.15. Recall that the goal of the centralized arbitrator is to optimize the spatial use of spectrum resources. The challenge a centralized arbitrator faces with a case like Figure 2.15 is when a certain frequency is interfered with in a certain area, which may require tapping into a larger pool of frequency bands. Figure 3.12 shows how the frequency band f3 can suffer from interference, as illustrated with the background large circle indicating the estimated inference area of frequency band f3 forcing the central arbitrator to tap into an additional frequency band f4, as indicated by the white circle.
Figure 3.12 A centralized arbitrator use of a larger frequency pool to overcome interference.
Decision fusion can occur at the local, distributed or centralized level. It all depends on the system under design. A good DSA design would create appropriate fusions at the appropriate level and share information in an optimal way to make the best use of spectrum resources. DSA is not a single solution to a single problem. Communication systems are complex and bounded by requirements, legacy systems interfacing with more up‐to‐date systems, and other dynamics that can influence the DSA design, information fusion, and decision making. The first three chapters of this book are intended to help the reader gain a broad understanding of DSA design challenges and how to approach DSA design for a given system. The next chapter covers examples of hybrid decision fusion cases and how decision fusion results can be leveraged for other cognitive capabilities such as reactive routing. Chapter 4 will give the reader an idea on how to design a hybrid DSA system while making the appropriate decision fusion local, distributed or centralized considering that DSA is part of the bigger goal of developing cognitive networks.
3.4 Concluding Remarks
The previous chapter covered the foundations of sensing techniques. This chapter builds on the previous chapter, covering ROC methodology and the foundations of DSA decision fusion techniques based on the ROC models. Two distinct ROC models were presented. The first is for sensing if a frequency band is occupied or not, which can be performed by an augmented sensor. This ROC model has its own challenges, including addressing the tradeoff between the probability of false alarm and the probability of misdetection. The second ROC model presented in this chapter is the same‐channel in‐band sensing model. The chapter examined how the ROC model can be best utilized to detect interference with the in‐band signal taking advantages of certain signal characteristics such as a constant envelope. This chapter also covered building on the ROC model to generate local decision fusions for augmented sensing and for same‐channel in‐band sensing, and extending decision fusion to the spatial dimension and to distributed cooperative and centralized decision fusion. The next chapter covers creating a hybrid cognitive network decision fusion design, building on the foundations covered in this chapter and the previous chapters.
3.5 Exercises
1 Consider the binary antipodal signal detection model in the figure below where μ0 is the mean of the PDF expressing the receipt of the signal y when the symbol S0 is transmitted, and μ1 is the mean of the PDF expressing the receipt of the signal y when the symbol S1 is transmitted. The channel is assumed to introduce AWGN with variance σ and the two small shaded areas express the error probability on both sides of the decision threshold 0. The left‐hand part of the shaded area expresses the error probability when decoding S0 but S1 was transmitted, while the right hand part of the shaded area expresses the error probability when decoding S1 but S0 was transmitted. State some of the parallels (similarities) and differences between this binary antipodal signal detection model and the ROC basic model explained in Section 3.1.
2 Using Equations (3.7) and (3.8) drive the equivalent to Equations (3.7)
